BLIND SEPARATION OF MIXEDKURTOSIS SIGNALS USING AN ADAPTIVE THRESHOLD NONLINEARITY
Heinz Mathis, Thomas P. von Hoff, and Marcel Joho
mathis@isi.ee.ethz.ch, vonhoff@isi.ee.ethz.ch, joho@isi.ee.ethz.ch
A parameterized threshold nonlinearity, which separates a mix
ture of signals with any distribution (except for Gaussian), is intro
duced. This nonlinearity is particularly simple to implement, since
it neither uses hyperbolic nor polynomial functions, unlike most
nonlinearities used for blind separation. For some specific distri
butions, the stable region of the threshold parameter is derived,
and optimal values for best separation performance are given. If
the threshold parameter is made adaptive during the separation
process, the successful separation of signals whose distribution
is unknown is demonstrated and compared against other known
methods.